Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations9879
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 MiB
Average record size in memory698.5 B

Variable types

Numeric7
Categorical17
Text3

Alerts

AverageSpeed is highly overall correlated with CircuitIDHigh correlation
CircuitID is highly overall correlated with AverageSpeed and 2 other fieldsHigh correlation
ConstructorName is highly overall correlated with ConstructorNationality and 1 other fieldsHigh correlation
ConstructorNationality is highly overall correlated with ConstructorName and 1 other fieldsHigh correlation
DriverNationality is highly overall correlated with NationalityHigh correlation
Grid is highly overall correlated with Points and 1 other fieldsHigh correlation
Nationality is highly overall correlated with DriverNationalityHigh correlation
Points is highly overall correlated with Grid and 1 other fieldsHigh correlation
Position is highly overall correlated with Grid and 1 other fieldsHigh correlation
Round is highly overall correlated with CircuitID and 1 other fieldsHigh correlation
Top3Last5Races is highly overall correlated with Top5Last10Races and 3 other fieldsHigh correlation
Top5Last10Races is highly overall correlated with Top3Last5RacesHigh correlation
WonLast10Races is highly overall correlated with WonLast7RacesHigh correlation
WonLast2Races is highly overall correlated with Top3Last5Races and 3 other fieldsHigh correlation
WonLast3Races is highly overall correlated with Top3Last5Races and 4 other fieldsHigh correlation
WonLast4Races is highly overall correlated with Top3Last5Races and 5 other fieldsHigh correlation
WonLast5Races is highly overall correlated with WonLast2Races and 4 other fieldsHigh correlation
WonLast6Races is highly overall correlated with WonLast3Races and 3 other fieldsHigh correlation
WonLast7Races is highly overall correlated with WonLast10Races and 3 other fieldsHigh correlation
constructorId is highly overall correlated with ConstructorName and 1 other fieldsHigh correlation
country is highly overall correlated with CircuitID and 1 other fieldsHigh correlation
WonLastRace is highly imbalanced (73.6%)Imbalance
WonLast2Races is highly imbalanced (56.6%)Imbalance
WonLast3Races is highly imbalanced (69.5%)Imbalance
WonLast4Races is highly imbalanced (76.9%)Imbalance
WonLast5Races is highly imbalanced (82.1%)Imbalance
WonLast6Races is highly imbalanced (86.2%)Imbalance
WonLast7Races is highly imbalanced (90.0%)Imbalance
WonLast10Races is highly imbalanced (95.9%)Imbalance
Top3Last5Races is highly imbalanced (50.6%)Imbalance
Top5Last10Races is highly imbalanced (67.3%)Imbalance
Points has 5641 (57.1%) zerosZeros
AverageSpeed has 1819 (18.4%) zerosZeros
DifferenceGridPosition has 1251 (12.7%) zerosZeros

Reproduction

Analysis started2024-08-13 00:03:19.870201
Analysis finished2024-08-13 00:03:27.048085
Duration7.18 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Season
Real number (ℝ)

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.2261
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:27.132859image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12006
median2012
Q32018
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0447821
Coefficient of variation (CV)0.0035009892
Kurtosis-1.1461237
Mean2012.2261
Median Absolute Deviation (MAD)6
Skewness-0.064265382
Sum19878782
Variance49.628954
MonotonicityIncreasing
2024-08-12T21:03:27.246741image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2012 480
 
4.9%
2016 462
 
4.7%
2010 456
 
4.6%
2011 456
 
4.6%
2023 440
 
4.5%
2022 440
 
4.5%
2021 440
 
4.5%
2018 420
 
4.3%
2019 420
 
4.3%
2013 418
 
4.2%
Other values (15) 5447
55.1%
ValueCountFrequency (%)
2000 373
3.8%
2001 374
3.8%
2002 362
3.7%
2003 320
3.2%
2004 360
3.6%
2005 376
3.8%
2006 396
4.0%
2007 374
3.8%
2008 368
3.7%
2009 340
3.4%
ValueCountFrequency (%)
2024 279
2.8%
2023 440
4.5%
2022 440
4.5%
2021 440
4.5%
2020 340
3.4%
2019 420
4.3%
2018 420
4.3%
2017 400
4.0%
2016 462
4.7%
2015 378
3.8%

Round
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9654823
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:27.353841image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum22
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.5804524
Coefficient of variation (CV)0.55997815
Kurtosis-1.0649226
Mean9.9654823
Median Absolute Deviation (MAD)5
Skewness0.10585767
Sum98449
Variance31.141449
MonotonicityNot monotonic
2024-08-12T21:03:27.454808image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 530
 
5.4%
2 530
 
5.4%
3 529
 
5.4%
1 528
 
5.3%
11 528
 
5.3%
7 528
 
5.3%
8 528
 
5.3%
9 528
 
5.3%
10 527
 
5.3%
12 527
 
5.3%
Other values (12) 4596
46.5%
ValueCountFrequency (%)
1 528
5.3%
2 530
5.4%
3 529
5.4%
4 530
5.4%
5 526
5.3%
6 525
5.3%
7 528
5.3%
8 528
5.3%
9 528
5.3%
10 527
5.3%
ValueCountFrequency (%)
22 60
 
0.6%
21 122
 
1.2%
20 166
 
1.7%
19 296
3.0%
18 356
3.6%
17 482
4.9%
16 505
5.1%
15 506
5.1%
14 526
5.3%
13 526
5.3%

CircuitID
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size554.1 KiB
silverstone
 
547
catalunya
 
528
hungaroring
 
526
monza
 
506
monaco
 
506
Other values (33)
7266 

Length

Max length14
Median length12
Mean length8.4233222
Min length3

Characters and Unicode

Total characters83214
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalbert_park
2nd rowalbert_park
3rd rowalbert_park
4th rowalbert_park
5th rowalbert_park

Common Values

ValueCountFrequency (%)
silverstone 547
 
5.5%
catalunya 528
 
5.3%
hungaroring 526
 
5.3%
monza 506
 
5.1%
monaco 506
 
5.1%
albert_park 487
 
4.9%
spa 484
 
4.9%
interlagos 484
 
4.9%
villeneuve 468
 
4.7%
suzuka 444
 
4.5%
Other values (28) 4899
49.6%

Length

2024-08-12T21:03:27.567344image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
silverstone 547
 
5.5%
catalunya 528
 
5.3%
hungaroring 526
 
5.3%
monza 506
 
5.1%
monaco 506
 
5.1%
albert_park 487
 
4.9%
spa 484
 
4.9%
interlagos 484
 
4.9%
villeneuve 468
 
4.7%
suzuka 444
 
4.5%
Other values (28) 4899
49.6%

Most occurring characters

ValueCountFrequency (%)
a 11582
13.9%
n 8339
 
10.0%
r 6875
 
8.3%
i 6553
 
7.9%
e 5521
 
6.6%
o 4639
 
5.6%
s 4573
 
5.5%
l 4502
 
5.4%
u 4088
 
4.9%
g 3933
 
4.7%
Other values (14) 22609
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81223
97.6%
Connector Punctuation 1991
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11582
14.3%
n 8339
10.3%
r 6875
 
8.5%
i 6553
 
8.1%
e 5521
 
6.8%
o 4639
 
5.7%
s 4573
 
5.6%
l 4502
 
5.5%
u 4088
 
5.0%
g 3933
 
4.8%
Other values (13) 20618
25.4%
Connector Punctuation
ValueCountFrequency (%)
_ 1991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81223
97.6%
Common 1991
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11582
14.3%
n 8339
10.3%
r 6875
 
8.5%
i 6553
 
8.1%
e 5521
 
6.8%
o 4639
 
5.7%
s 4573
 
5.6%
l 4502
 
5.5%
u 4088
 
5.0%
g 3933
 
4.8%
Other values (13) 20618
25.4%
Common
ValueCountFrequency (%)
_ 1991
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11582
13.9%
n 8339
 
10.0%
r 6875
 
8.3%
i 6553
 
7.9%
e 5521
 
6.6%
o 4639
 
5.6%
s 4573
 
5.5%
l 4502
 
5.4%
u 4088
 
4.9%
g 3933
 
4.7%
Other values (14) 22609
27.2%

country
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size530.8 KiB
Italy
754 
Spain
 
640
Germany
 
554
UK
 
547
Hungary
 
526
Other values (25)
6858 

Length

Max length13
Median length11
Mean length6.0114384
Min length2

Characters and Unicode

Total characters59387
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralia
2nd rowAustralia
3rd rowAustralia
4th rowAustralia
5th rowAustralia

Common Values

ValueCountFrequency (%)
Italy 754
 
7.6%
Spain 640
 
6.5%
Germany 554
 
5.6%
UK 547
 
5.5%
Hungary 526
 
5.3%
Monaco 506
 
5.1%
Australia 487
 
4.9%
Japan 486
 
4.9%
Brazil 484
 
4.9%
Belgium 484
 
4.9%
Other values (20) 4411
44.7%

Length

2024-08-12T21:03:27.680880image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
italy 754
 
7.6%
spain 640
 
6.4%
germany 554
 
5.6%
uk 547
 
5.5%
hungary 526
 
5.3%
monaco 506
 
5.1%
australia 487
 
4.9%
japan 486
 
4.9%
brazil 484
 
4.9%
belgium 484
 
4.9%
Other values (22) 4511
45.2%

Most occurring characters

ValueCountFrequency (%)
a 11195
18.9%
n 4842
 
8.2%
i 4652
 
7.8%
r 4057
 
6.8%
l 2699
 
4.5%
y 2416
 
4.1%
e 2356
 
4.0%
u 2322
 
3.9%
A 1829
 
3.1%
t 1791
 
3.0%
Other values (31) 21228
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47221
79.5%
Uppercase Letter 12066
 
20.3%
Space Separator 100
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11195
23.7%
n 4842
10.3%
i 4652
9.9%
r 4057
 
8.6%
l 2699
 
5.7%
y 2416
 
5.1%
e 2356
 
5.0%
u 2322
 
4.9%
t 1791
 
3.8%
s 1633
 
3.5%
Other values (12) 9258
19.6%
Uppercase Letter
ValueCountFrequency (%)
A 1829
15.2%
S 1492
12.4%
B 1408
11.7%
U 1337
11.1%
M 1058
8.8%
C 830
6.9%
I 824
6.8%
K 641
 
5.3%
G 554
 
4.6%
H 526
 
4.4%
Other values (8) 1567
13.0%
Space Separator
ValueCountFrequency (%)
100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59287
99.8%
Common 100
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11195
18.9%
n 4842
 
8.2%
i 4652
 
7.8%
r 4057
 
6.8%
l 2699
 
4.6%
y 2416
 
4.1%
e 2356
 
4.0%
u 2322
 
3.9%
A 1829
 
3.1%
t 1791
 
3.0%
Other values (30) 21128
35.6%
Common
ValueCountFrequency (%)
100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11195
18.9%
n 4842
 
8.2%
i 4652
 
7.8%
r 4057
 
6.8%
l 2699
 
4.5%
y 2416
 
4.1%
e 2356
 
4.0%
u 2322
 
3.9%
A 1829
 
3.1%
t 1791
 
3.0%
Other values (31) 21228
35.7%

ConstructorName
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size549.4 KiB
Ferrari
938 
McLaren
938 
Williams
937 
Red Bull
768 
Sauber
590 
Other values (33)
5708 

Length

Max length14
Median length12
Mean length7.9355198
Min length3

Characters and Unicode

Total characters78395
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArrows
2nd rowArrows
3rd rowBAR
4th rowBAR
5th rowBenetton

Common Values

ValueCountFrequency (%)
Ferrari 938
 
9.5%
McLaren 938
 
9.5%
Williams 937
 
9.5%
Red Bull 768
 
7.8%
Sauber 590
 
6.0%
Mercedes 590
 
6.0%
Renault 556
 
5.6%
Toro Rosso 536
 
5.4%
Force India 424
 
4.3%
Haas F1 Team 360
 
3.6%
Other values (28) 3242
32.8%

Length

2024-08-12T21:03:27.795573image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ferrari 938
 
6.9%
mclaren 938
 
6.9%
williams 937
 
6.9%
red 768
 
5.6%
bull 768
 
5.6%
sauber 730
 
5.4%
f1 702
 
5.2%
mercedes 590
 
4.3%
renault 556
 
4.1%
team 548
 
4.0%
Other values (35) 6130
45.1%

Most occurring characters

ValueCountFrequency (%)
a 8417
 
10.7%
r 7878
 
10.0%
e 7408
 
9.4%
i 4703
 
6.0%
o 4629
 
5.9%
l 4500
 
5.7%
s 3882
 
5.0%
3726
 
4.8%
n 3394
 
4.3%
u 2963
 
3.8%
Other values (28) 26895
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58238
74.3%
Uppercase Letter 15693
 
20.0%
Space Separator 3726
 
4.8%
Decimal Number 738
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8417
14.5%
r 7878
13.5%
e 7408
12.7%
i 4703
8.1%
o 4629
7.9%
l 4500
7.7%
s 3882
6.7%
n 3394
5.8%
u 2963
 
5.1%
d 2302
 
4.0%
Other values (11) 8162
14.0%
Uppercase Letter
ValueCountFrequency (%)
R 2492
15.9%
M 2335
14.9%
F 2100
13.4%
T 1646
10.5%
B 1242
7.9%
L 1168
7.4%
W 1077
6.9%
A 1068
6.8%
S 850
 
5.4%
H 582
 
3.7%
Other values (5) 1133
7.2%
Space Separator
ValueCountFrequency (%)
3726
100.0%
Decimal Number
ValueCountFrequency (%)
1 738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73931
94.3%
Common 4464
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8417
 
11.4%
r 7878
 
10.7%
e 7408
 
10.0%
i 4703
 
6.4%
o 4629
 
6.3%
l 4500
 
6.1%
s 3882
 
5.3%
n 3394
 
4.6%
u 2963
 
4.0%
R 2492
 
3.4%
Other values (26) 23665
32.0%
Common
ValueCountFrequency (%)
3726
83.5%
1 738
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8417
 
10.7%
r 7878
 
10.0%
e 7408
 
9.4%
i 4703
 
6.0%
o 4629
 
5.9%
l 4500
 
5.7%
s 3882
 
5.0%
3726
 
4.8%
n 3394
 
4.3%
u 2963
 
3.8%
Other values (28) 26895
34.3%

constructorId
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size543.7 KiB
ferrari
938 
mclaren
938 
williams
937 
red_bull
768 
sauber
590 
Other values (33)
5708 

Length

Max length12
Median length11
Mean length7.3482134
Min length2

Characters and Unicode

Total characters72593
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarrows
2nd rowarrows
3rd rowbar
4th rowbar
5th rowbenetton

Common Values

ValueCountFrequency (%)
ferrari 938
 
9.5%
mclaren 938
 
9.5%
williams 937
 
9.5%
red_bull 768
 
7.8%
sauber 590
 
6.0%
mercedes 590
 
6.0%
renault 556
 
5.6%
toro_rosso 536
 
5.4%
force_india 424
 
4.3%
haas 360
 
3.6%
Other values (28) 3242
32.8%

Length

2024-08-12T21:03:27.907308image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ferrari 938
 
9.5%
mclaren 938
 
9.5%
williams 937
 
9.5%
red_bull 768
 
7.8%
sauber 590
 
6.0%
mercedes 590
 
6.0%
renault 556
 
5.6%
toro_rosso 536
 
5.4%
force_india 424
 
4.3%
haas 360
 
3.6%
Other values (28) 3242
32.8%

Most occurring characters

ValueCountFrequency (%)
r 10160
14.0%
a 8857
12.2%
e 6652
 
9.2%
l 5668
 
7.8%
i 5125
 
7.1%
s 4576
 
6.3%
o 4213
 
5.8%
n 3470
 
4.8%
m 3306
 
4.6%
u 2885
 
4.0%
Other values (15) 17681
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69983
96.4%
Connector Punctuation 2420
 
3.3%
Decimal Number 190
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10160
14.5%
a 8857
12.7%
e 6652
9.5%
l 5668
 
8.1%
i 5125
 
7.3%
s 4576
 
6.5%
o 4213
 
6.0%
n 3470
 
5.0%
m 3306
 
4.7%
u 2885
 
4.1%
Other values (13) 15071
21.5%
Connector Punctuation
ValueCountFrequency (%)
_ 2420
100.0%
Decimal Number
ValueCountFrequency (%)
1 190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69983
96.4%
Common 2610
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10160
14.5%
a 8857
12.7%
e 6652
9.5%
l 5668
 
8.1%
i 5125
 
7.3%
s 4576
 
6.5%
o 4213
 
6.0%
n 3470
 
5.0%
m 3306
 
4.7%
u 2885
 
4.1%
Other values (13) 15071
21.5%
Common
ValueCountFrequency (%)
_ 2420
92.7%
1 190
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72593
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10160
14.0%
a 8857
12.2%
e 6652
 
9.2%
l 5668
 
7.8%
i 5125
 
7.1%
s 4576
 
6.3%
o 4213
 
5.8%
n 3470
 
4.8%
m 3306
 
4.6%
u 2885
 
4.0%
Other values (15) 17681
24.4%

ConstructorNationality
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size538.4 KiB
British
2919 
Italian
1942 
Swiss
798 
French
783 
Austrian
768 
Other values (9)
2669 

Length

Max length9
Median length7
Mean length6.7909707
Min length5

Characters and Unicode

Total characters67088
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBritish
2nd rowBritish
3rd rowBritish
4th rowBritish
5th rowItalian

Common Values

ValueCountFrequency (%)
British 2919
29.5%
Italian 1942
19.7%
Swiss 798
 
8.1%
French 783
 
7.9%
Austrian 768
 
7.8%
German 730
 
7.4%
Japanese 464
 
4.7%
Indian 424
 
4.3%
American 360
 
3.6%
Irish 208
 
2.1%
Other values (4) 483
 
4.9%

Length

2024-08-12T21:03:28.025937image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
british 2919
29.5%
italian 1942
19.7%
swiss 798
 
8.1%
french 783
 
7.9%
austrian 768
 
7.8%
german 730
 
7.4%
japanese 464
 
4.7%
indian 424
 
4.3%
american 360
 
3.6%
irish 208
 
2.1%
Other values (4) 483
 
4.9%

Most occurring characters

ValueCountFrequency (%)
i 10779
16.1%
a 7911
11.8%
s 6533
9.7%
n 6336
9.4%
r 5768
8.6%
t 5671
8.5%
h 4068
 
6.1%
B 2919
 
4.4%
e 2801
 
4.2%
I 2574
 
3.8%
Other values (16) 11728
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57209
85.3%
Uppercase Letter 9879
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 10779
18.8%
a 7911
13.8%
s 6533
11.4%
n 6336
11.1%
r 5768
10.1%
t 5671
9.9%
h 4068
 
7.1%
e 2801
 
4.9%
l 2130
 
3.7%
c 1185
 
2.1%
Other values (6) 4027
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
B 2919
29.5%
I 2574
26.1%
A 1128
 
11.4%
S 914
 
9.3%
F 783
 
7.9%
G 730
 
7.4%
J 464
 
4.7%
M 188
 
1.9%
R 137
 
1.4%
D 42
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 67088
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 10779
16.1%
a 7911
11.8%
s 6533
9.7%
n 6336
9.4%
r 5768
8.6%
t 5671
8.5%
h 4068
 
6.1%
B 2919
 
4.4%
e 2801
 
4.2%
I 2574
 
3.8%
Other values (16) 11728
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 10779
16.1%
a 7911
11.8%
s 6533
9.7%
n 6336
9.4%
r 5768
8.6%
t 5671
8.5%
h 4068
 
6.1%
B 2919
 
4.4%
e 2801
 
4.2%
I 2574
 
3.8%
Other values (16) 11728
17.5%
Distinct124
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
2024-08-12T21:03:28.217465image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length18
Median length15
Mean length7.6299221
Min length3

Characters and Unicode

Total characters75376
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowrosa
2nd rowverstappen
3rd rowvilleneuve
4th rowzonta
5th rowfisichella
ValueCountFrequency (%)
alonso 394
 
4.0%
raikkonen 352
 
3.6%
hamilton 346
 
3.5%
button 309
 
3.1%
vettel 300
 
3.0%
perez 273
 
2.8%
massa 271
 
2.7%
ricciardo 253
 
2.6%
bottas 237
 
2.4%
hulkenberg 220
 
2.2%
Other values (114) 6924
70.1%
2024-08-12T21:03:28.540901image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 7405
 
9.8%
a 7240
 
9.6%
r 5892
 
7.8%
n 5684
 
7.5%
o 5649
 
7.5%
l 5418
 
7.2%
i 5202
 
6.9%
s 4982
 
6.6%
t 4096
 
5.4%
c 2868
 
3.8%
Other values (17) 20940
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74460
98.8%
Connector Punctuation 916
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7405
 
9.9%
a 7240
 
9.7%
r 5892
 
7.9%
n 5684
 
7.6%
o 5649
 
7.6%
l 5418
 
7.3%
i 5202
 
7.0%
s 4982
 
6.7%
t 4096
 
5.5%
c 2868
 
3.9%
Other values (16) 20024
26.9%
Connector Punctuation
ValueCountFrequency (%)
_ 916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74460
98.8%
Common 916
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7405
 
9.9%
a 7240
 
9.7%
r 5892
 
7.9%
n 5684
 
7.6%
o 5649
 
7.6%
l 5418
 
7.3%
i 5202
 
7.0%
s 4982
 
6.7%
t 4096
 
5.5%
c 2868
 
3.9%
Other values (16) 20024
26.9%
Common
ValueCountFrequency (%)
_ 916
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7405
 
9.8%
a 7240
 
9.6%
r 5892
 
7.8%
n 5684
 
7.5%
o 5649
 
7.5%
l 5418
 
7.2%
i 5202
 
6.9%
s 4982
 
6.6%
t 4096
 
5.4%
c 2868
 
3.8%
Other values (17) 20940
27.8%
Distinct113
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size535.4 KiB
2024-08-12T21:03:28.774277image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.9385565
Min length3

Characters and Unicode

Total characters58667
Distinct characters55
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPedro
2nd rowJos
3rd rowJacques
4th rowRicardo
5th rowGiancarlo
ValueCountFrequency (%)
nico 426
 
4.3%
fernando 394
 
4.0%
kimi 352
 
3.6%
lewis 346
 
3.5%
felipe 311
 
3.1%
jenson 309
 
3.1%
sebastian 300
 
3.0%
sergio 273
 
2.8%
daniel 253
 
2.6%
valtteri 237
 
2.4%
Other values (103) 6678
67.6%
2024-08-12T21:03:29.120352image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6475
 
11.0%
i 6423
 
10.9%
e 5737
 
9.8%
n 5091
 
8.7%
o 3801
 
6.5%
r 3634
 
6.2%
s 2593
 
4.4%
l 2522
 
4.3%
t 1922
 
3.3%
c 1701
 
2.9%
Other values (45) 18768
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48550
82.8%
Uppercase Letter 9998
 
17.0%
Dash Punctuation 119
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6475
13.3%
i 6423
13.2%
e 5737
11.8%
n 5091
10.5%
o 3801
7.8%
r 3634
7.5%
s 2593
 
5.3%
l 2522
 
5.2%
t 1922
 
4.0%
c 1701
 
3.5%
Other values (21) 8651
17.8%
Uppercase Letter
ValueCountFrequency (%)
J 1027
 
10.3%
M 861
 
8.6%
N 785
 
7.9%
S 767
 
7.7%
F 712
 
7.1%
R 698
 
7.0%
L 698
 
7.0%
K 653
 
6.5%
D 522
 
5.2%
C 501
 
5.0%
Other values (13) 2774
27.7%
Dash Punctuation
ValueCountFrequency (%)
- 119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58548
99.8%
Common 119
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6475
 
11.1%
i 6423
 
11.0%
e 5737
 
9.8%
n 5091
 
8.7%
o 3801
 
6.5%
r 3634
 
6.2%
s 2593
 
4.4%
l 2522
 
4.3%
t 1922
 
3.3%
c 1701
 
2.9%
Other values (44) 18649
31.9%
Common
ValueCountFrequency (%)
- 119
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58439
99.6%
None 228
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6475
 
11.1%
i 6423
 
11.0%
e 5737
 
9.8%
n 5091
 
8.7%
o 3801
 
6.5%
r 3634
 
6.2%
s 2593
 
4.4%
l 2522
 
4.3%
t 1922
 
3.3%
c 1701
 
2.9%
Other values (39) 18540
31.7%
None
ValueCountFrequency (%)
é 103
45.2%
É 58
25.4%
ô 40
 
17.5%
ó 21
 
9.2%
á 3
 
1.3%
Å¡ 3
 
1.3%

DriverNationality
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size541.8 KiB
German
1590 
British
1324 
Finnish
769 
Brazilian
766 
Spanish
762 
Other values (29)
4668 

Length

Max length13
Median length10
Mean length7.1424233
Min length4

Characters and Unicode

Total characters70560
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpanish
2nd rowDutch
3rd rowCanadian
4th rowBrazilian
5th rowItalian

Common Values

ValueCountFrequency (%)
German 1590
16.1%
British 1324
13.4%
Finnish 769
 
7.8%
Brazilian 766
 
7.8%
Spanish 762
 
7.7%
French 738
 
7.5%
Italian 561
 
5.7%
Australian 506
 
5.1%
Dutch 336
 
3.4%
Mexican 332
 
3.4%
Other values (24) 2195
22.2%

Length

2024-08-12T21:03:29.238262image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
german 1590
16.0%
british 1324
13.4%
finnish 769
 
7.8%
brazilian 766
 
7.7%
spanish 762
 
7.7%
french 738
 
7.4%
italian 561
 
5.7%
australian 506
 
5.1%
dutch 336
 
3.4%
mexican 332
 
3.4%
Other values (25) 2225
22.5%

Most occurring characters

ValueCountFrequency (%)
i 9461
13.4%
a 9189
13.0%
n 9127
12.9%
r 5210
 
7.4%
s 4956
 
7.0%
h 4478
 
6.3%
e 4406
 
6.2%
t 2881
 
4.1%
l 2231
 
3.2%
B 2152
 
3.0%
Other values (30) 16469
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60621
85.9%
Uppercase Letter 9909
 
14.0%
Space Separator 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9461
15.6%
a 9189
15.2%
n 9127
15.1%
r 5210
8.6%
s 4956
8.2%
h 4478
7.4%
e 4406
7.3%
t 2881
 
4.8%
l 2231
 
3.7%
m 1753
 
2.9%
Other values (12) 6929
11.4%
Uppercase Letter
ValueCountFrequency (%)
B 2152
21.7%
G 1590
16.0%
F 1507
15.2%
S 914
9.2%
A 691
 
7.0%
I 646
 
6.5%
D 519
 
5.2%
M 487
 
4.9%
C 475
 
4.8%
J 308
 
3.1%
Other values (7) 620
 
6.3%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70530
> 99.9%
Common 30
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9461
13.4%
a 9189
13.0%
n 9127
12.9%
r 5210
 
7.4%
s 4956
 
7.0%
h 4478
 
6.3%
e 4406
 
6.2%
t 2881
 
4.1%
l 2231
 
3.2%
B 2152
 
3.1%
Other values (29) 16439
23.3%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9461
13.4%
a 9189
13.0%
n 9127
12.9%
r 5210
 
7.4%
s 4956
 
7.0%
h 4478
 
6.3%
e 4406
 
6.2%
t 2881
 
4.1%
l 2231
 
3.2%
B 2152
 
3.0%
Other values (30) 16469
23.3%

Nationality
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size541.8 KiB
German
1590 
British
1324 
Finnish
769 
Brazilian
766 
Spanish
762 
Other values (29)
4668 

Length

Max length13
Median length10
Mean length7.1424233
Min length4

Characters and Unicode

Total characters70560
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpanish
2nd rowDutch
3rd rowCanadian
4th rowBrazilian
5th rowItalian

Common Values

ValueCountFrequency (%)
German 1590
16.1%
British 1324
13.4%
Finnish 769
 
7.8%
Brazilian 766
 
7.8%
Spanish 762
 
7.7%
French 738
 
7.5%
Italian 561
 
5.7%
Australian 506
 
5.1%
Dutch 336
 
3.4%
Mexican 332
 
3.4%
Other values (24) 2195
22.2%

Length

2024-08-12T21:03:29.347969image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
german 1590
16.0%
british 1324
13.4%
finnish 769
 
7.8%
brazilian 766
 
7.7%
spanish 762
 
7.7%
french 738
 
7.4%
italian 561
 
5.7%
australian 506
 
5.1%
dutch 336
 
3.4%
mexican 332
 
3.4%
Other values (25) 2225
22.5%

Most occurring characters

ValueCountFrequency (%)
i 9461
13.4%
a 9189
13.0%
n 9127
12.9%
r 5210
 
7.4%
s 4956
 
7.0%
h 4478
 
6.3%
e 4406
 
6.2%
t 2881
 
4.1%
l 2231
 
3.2%
B 2152
 
3.0%
Other values (30) 16469
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60621
85.9%
Uppercase Letter 9909
 
14.0%
Space Separator 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9461
15.6%
a 9189
15.2%
n 9127
15.1%
r 5210
8.6%
s 4956
8.2%
h 4478
7.4%
e 4406
7.3%
t 2881
 
4.8%
l 2231
 
3.7%
m 1753
 
2.9%
Other values (12) 6929
11.4%
Uppercase Letter
ValueCountFrequency (%)
B 2152
21.7%
G 1590
16.0%
F 1507
15.2%
S 914
9.2%
A 691
 
7.0%
I 646
 
6.5%
D 519
 
5.2%
M 487
 
4.9%
C 475
 
4.8%
J 308
 
3.1%
Other values (7) 620
 
6.3%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70530
> 99.9%
Common 30
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9461
13.4%
a 9189
13.0%
n 9127
12.9%
r 5210
 
7.4%
s 4956
 
7.0%
h 4478
 
6.3%
e 4406
 
6.2%
t 2881
 
4.1%
l 2231
 
3.2%
B 2152
 
3.1%
Other values (29) 16439
23.3%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9461
13.4%
a 9189
13.0%
n 9127
12.9%
r 5210
 
7.4%
s 4956
 
7.0%
h 4478
 
6.3%
e 4406
 
6.2%
t 2881
 
4.1%
l 2231
 
3.2%
B 2152
 
3.0%
Other values (30) 16469
23.3%

Position
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.081081
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:29.454622image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile21
Maximum24
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.1614905
Coefficient of variation (CV)0.55603695
Kurtosis-1.1269268
Mean11.081081
Median Absolute Deviation (MAD)5
Skewness0.05152827
Sum109470
Variance37.963966
MonotonicityNot monotonic
2024-08-12T21:03:29.564259image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11 469
 
4.7%
14 469
 
4.7%
9 469
 
4.7%
16 469
 
4.7%
13 469
 
4.7%
8 469
 
4.7%
17 469
 
4.7%
18 469
 
4.7%
15 469
 
4.7%
12 469
 
4.7%
Other values (14) 5189
52.5%
ValueCountFrequency (%)
1 469
4.7%
2 469
4.7%
3 469
4.7%
4 469
4.7%
5 469
4.7%
6 469
4.7%
7 469
4.7%
8 469
4.7%
9 469
4.7%
10 469
4.7%
ValueCountFrequency (%)
24 58
 
0.6%
23 58
 
0.6%
22 195
2.0%
21 199
2.0%
20 463
4.7%
19 464
4.7%
18 469
4.7%
17 469
4.7%
16 469
4.7%
15 469
4.7%

Points
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6493572
Minimum0
Maximum50
Zeros5641
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:29.673167image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile18
Maximum50
Range50
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.113076
Coefficient of variation (CV)1.6751103
Kurtosis3.5194619
Mean3.6493572
Median Absolute Deviation (MAD)0
Skewness1.9649679
Sum36052
Variance37.369698
MonotonicityNot monotonic
2024-08-12T21:03:29.791665image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 5641
57.1%
2 467
 
4.7%
1 466
 
4.7%
4 465
 
4.7%
6 459
 
4.6%
10 458
 
4.6%
8 411
 
4.2%
12 284
 
2.9%
15 283
 
2.9%
18 270
 
2.7%
Other values (20) 675
 
6.8%
ValueCountFrequency (%)
0 5641
57.1%
0.5 2
 
< 0.1%
1 466
 
4.7%
1.5 1
 
< 0.1%
2 467
 
4.7%
2.5 1
 
< 0.1%
3 177
 
1.8%
4 465
 
4.7%
5 128
 
1.3%
6 459
 
4.6%
ValueCountFrequency (%)
50 1
 
< 0.1%
36 1
 
< 0.1%
30 1
 
< 0.1%
26 35
 
0.4%
25 258
2.6%
24 1
 
< 0.1%
20 1
 
< 0.1%
19 23
 
0.2%
18 270
2.7%
16 11
 
0.1%

Grid
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.916793
Minimum0
Maximum24
Zeros87
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:29.902141image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q316
95-th percentile21
Maximum24
Range24
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.1934758
Coefficient of variation (CV)0.56733471
Kurtosis-1.1149908
Mean10.916793
Median Absolute Deviation (MAD)5
Skewness0.054933939
Sum107847
Variance38.359142
MonotonicityNot monotonic
2024-08-12T21:03:30.416918image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
17 470
 
4.8%
9 469
 
4.7%
4 469
 
4.7%
3 469
 
4.7%
5 469
 
4.7%
6 469
 
4.7%
1 469
 
4.7%
12 468
 
4.7%
8 468
 
4.7%
14 468
 
4.7%
Other values (15) 5191
52.5%
ValueCountFrequency (%)
0 87
 
0.9%
1 469
4.7%
2 467
4.7%
3 469
4.7%
4 469
4.7%
5 469
4.7%
6 469
4.7%
7 468
4.7%
8 468
4.7%
9 469
4.7%
ValueCountFrequency (%)
24 54
 
0.5%
23 56
 
0.6%
22 190
1.9%
21 195
2.0%
20 418
4.2%
19 456
4.6%
18 464
4.7%
17 470
4.8%
16 467
4.7%
15 467
4.7%

Status
Text

Distinct104
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size546.1 KiB
2024-08-12T21:03:30.667250image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length16
Median length8
Mean length7.5883187
Min length3

Characters and Unicode

Total characters74965
Distinct characters58
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.3%

Sample

1st rowSuspension
2nd rowSuspension
3rd rowFinished
4th rowFinished
5th rowFinished
ValueCountFrequency (%)
finished 4579
34.5%
lap 2218
16.7%
1 2218
16.7%
laps 780
 
5.9%
2 530
 
4.0%
collision 464
 
3.5%
engine 304
 
2.3%
accident 259
 
2.0%
gearbox 157
 
1.2%
3 143
 
1.1%
Other values (102) 1626
 
12.2%
2024-08-12T21:03:30.994352image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 11426
15.2%
s 6558
 
8.7%
n 6492
 
8.7%
e 6359
 
8.5%
d 5175
 
6.9%
h 4765
 
6.4%
F 4626
 
6.2%
a 3851
 
5.1%
3399
 
4.5%
p 3263
 
4.4%
Other values (48) 19051
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55621
74.2%
Uppercase Letter 9931
 
13.2%
Space Separator 3399
 
4.5%
Decimal Number 3014
 
4.0%
Math Symbol 2998
 
4.0%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 11426
20.5%
s 6558
11.8%
n 6492
11.7%
e 6359
11.4%
d 5175
9.3%
h 4765
8.6%
a 3851
 
6.9%
p 3263
 
5.9%
o 1478
 
2.7%
l 1466
 
2.6%
Other values (15) 4788
8.6%
Uppercase Letter
ValueCountFrequency (%)
F 4626
46.6%
L 2999
30.2%
C 487
 
4.9%
E 390
 
3.9%
A 263
 
2.6%
S 238
 
2.4%
G 157
 
1.6%
H 122
 
1.2%
B 106
 
1.1%
R 93
 
0.9%
Other values (10) 450
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 2231
74.0%
2 533
 
17.7%
3 143
 
4.7%
4 65
 
2.2%
5 19
 
0.6%
6 8
 
0.3%
7 6
 
0.2%
0 4
 
0.1%
8 3
 
0.1%
9 2
 
0.1%
Space Separator
ValueCountFrequency (%)
3399
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2998
100.0%
Other Punctuation
ValueCountFrequency (%)
% 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65552
87.4%
Common 9413
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 11426
17.4%
s 6558
10.0%
n 6492
9.9%
e 6359
9.7%
d 5175
7.9%
h 4765
7.3%
F 4626
7.1%
a 3851
 
5.9%
p 3263
 
5.0%
L 2999
 
4.6%
Other values (35) 10038
15.3%
Common
ValueCountFrequency (%)
3399
36.1%
+ 2998
31.8%
1 2231
23.7%
2 533
 
5.7%
3 143
 
1.5%
4 65
 
0.7%
5 19
 
0.2%
6 8
 
0.1%
7 6
 
0.1%
0 4
 
< 0.1%
Other values (3) 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 11426
15.2%
s 6558
 
8.7%
n 6492
 
8.7%
e 6359
 
8.5%
d 5175
 
6.9%
h 4765
 
6.4%
F 4626
 
6.2%
a 3851
 
5.1%
3399
 
4.5%
p 3263
 
4.4%
Other values (48) 19051
25.4%

AverageSpeed
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7551
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.41417
Minimum0
Maximum257.32
Zeros1819
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:31.122011image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1169.427
median200.202
Q3213.935
95-th percentile235.2158
Maximum257.32
Range257.32
Interquartile range (IQR)44.508

Descriptive statistics

Standard deviation81.383176
Coefficient of variation (CV)0.48903994
Kurtosis0.32863497
Mean166.41417
Median Absolute Deviation (MAD)16.251
Skewness-1.4254981
Sum1644005.6
Variance6623.2214
MonotonicityNot monotonic
2024-08-12T21:03:31.261502image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1819
 
18.4%
207.069 4
 
< 0.1%
204.946 3
 
< 0.1%
189.423 3
 
< 0.1%
196.785 3
 
< 0.1%
201.527 3
 
< 0.1%
200.363 3
 
< 0.1%
188.806 3
 
< 0.1%
222.592 3
 
< 0.1%
202.685 3
 
< 0.1%
Other values (7541) 8032
81.3%
ValueCountFrequency (%)
0 1819
18.4%
89.54 1
 
< 0.1%
91.61 1
 
< 0.1%
100.615 1
 
< 0.1%
101.399 1
 
< 0.1%
101.884 1
 
< 0.1%
108.41 1
 
< 0.1%
112.116 1
 
< 0.1%
117.753 1
 
< 0.1%
118.872 1
 
< 0.1%
ValueCountFrequency (%)
257.32 1
< 0.1%
256.324 1
< 0.1%
255.874 1
< 0.1%
255.014 1
< 0.1%
254.861 1
< 0.1%
253.874 1
< 0.1%
253.566 1
< 0.1%
252.794 1
< 0.1%
252.77 1
< 0.1%
252.604 1
< 0.1%

DifferenceGridPosition
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16428788
Minimum-24
Maximum21
Zeros1251
Zeros (%)12.7%
Negative3765
Negative (%)38.1%
Memory size77.3 KiB
2024-08-12T21:03:31.398137image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum-24
5-th percentile-12
Q1-2
median0
Q33
95-th percentile8
Maximum21
Range45
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.825918
Coefficient of variation (CV)-35.461641
Kurtosis1.4797805
Mean-0.16428788
Median Absolute Deviation (MAD)3
Skewness-0.76937568
Sum-1623
Variance33.94132
MonotonicityNot monotonic
2024-08-12T21:03:31.555783image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 1251
12.7%
1 1039
 
10.5%
2 877
 
8.9%
-1 778
 
7.9%
3 666
 
6.7%
4 586
 
5.9%
-2 554
 
5.6%
5 477
 
4.8%
-3 434
 
4.4%
6 344
 
3.5%
Other values (35) 2873
29.1%
ValueCountFrequency (%)
-24 4
 
< 0.1%
-23 4
 
< 0.1%
-22 7
 
0.1%
-21 4
 
< 0.1%
-20 22
 
0.2%
-19 24
 
0.2%
-18 40
0.4%
-17 49
0.5%
-16 69
0.7%
-15 70
0.7%
ValueCountFrequency (%)
21 1
 
< 0.1%
19 3
 
< 0.1%
18 7
 
0.1%
17 7
 
0.1%
16 15
 
0.2%
15 14
 
0.1%
14 25
 
0.3%
13 26
0.3%
12 48
0.5%
11 64
0.6%

WonLastRace
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
9436 
1
 
443

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9436
95.5%
1 443
 
4.5%

Length

2024-08-12T21:03:31.733629image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:31.971992image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 9436
95.5%
1 443
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 9436
95.5%
1 443
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9436
95.5%
1 443
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9436
95.5%
1 443
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9436
95.5%
1 443
 
4.5%

WonLast2Races
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
8996 
1
 
883

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8996
91.1%
1 883
 
8.9%

Length

2024-08-12T21:03:32.070727image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:32.156498image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8996
91.1%
1 883
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 8996
91.1%
1 883
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8996
91.1%
1 883
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8996
91.1%
1 883
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8996
91.1%
1 883
 
8.9%

WonLast3Races
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
9340 
1
 
539

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9340
94.5%
1 539
 
5.5%

Length

2024-08-12T21:03:32.250248image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:32.334024image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 9340
94.5%
1 539
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 9340
94.5%
1 539
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9340
94.5%
1 539
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9340
94.5%
1 539
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9340
94.5%
1 539
 
5.5%

WonLast4Races
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
9508 
1
 
371

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9508
96.2%
1 371
 
3.8%

Length

2024-08-12T21:03:32.428771image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:32.511550image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 9508
96.2%
1 371
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 9508
96.2%
1 371
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9508
96.2%
1 371
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9508
96.2%
1 371
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9508
96.2%
1 371
 
3.8%

WonLast5Races
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
9612 
1
 
267

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9612
97.3%
1 267
 
2.7%

Length

2024-08-12T21:03:32.601310image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:32.684088image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 9612
97.3%
1 267
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 9612
97.3%
1 267
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9612
97.3%
1 267
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9612
97.3%
1 267
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9612
97.3%
1 267
 
2.7%

WonLast6Races
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
9688 
1
 
191

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9688
98.1%
1 191
 
1.9%

Length

2024-08-12T21:03:32.778842image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:32.872583image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 9688
98.1%
1 191
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 9688
98.1%
1 191
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9688
98.1%
1 191
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9688
98.1%
1 191
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9688
98.1%
1 191
 
1.9%

WonLast7Races
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
9751 
1
 
128

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9751
98.7%
1 128
 
1.3%

Length

2024-08-12T21:03:32.973315image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:33.066067image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 9751
98.7%
1 128
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 9751
98.7%
1 128
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9751
98.7%
1 128
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9751
98.7%
1 128
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9751
98.7%
1 128
 
1.3%

WonLast10Races
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
9835 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9835
99.6%
1 44
 
0.4%

Length

2024-08-12T21:03:33.157310image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:33.244076image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 9835
99.6%
1 44
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 9835
99.6%
1 44
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9835
99.6%
1 44
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9835
99.6%
1 44
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9835
99.6%
1 44
 
0.4%

Top3Last5Races
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
8813 
1
1066 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8813
89.2%
1 1066
 
10.8%

Length

2024-08-12T21:03:33.336830image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:33.421602image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 8813
89.2%
1 1066
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 8813
89.2%
1 1066
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8813
89.2%
1 1066
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8813
89.2%
1 1066
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8813
89.2%
1 1066
 
10.8%

Top5Last10Races
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
0
9288 
1
 
591

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9288
94.0%
1 591
 
6.0%

Length

2024-08-12T21:03:33.514354image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T21:03:33.604115image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
0 9288
94.0%
1 591
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 9288
94.0%
1 591
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9288
94.0%
1 591
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9288
94.0%
1 591
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9288
94.0%
1 591
 
6.0%

Interactions

2024-08-12T21:03:25.895340image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.166707image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.782078image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.350158image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.964663image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.577240image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.297862image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.987095image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.256468image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.865850image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.441476image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.050434image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.663564image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.384659image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:26.069873image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.341072image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.944640image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.539078image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.131218image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.750332image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.465413image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:26.153394image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.432827image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.026283image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.623855image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.212999image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.961766image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.554175image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:26.236040image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.519596image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.108065image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.710624image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.295777image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.047537image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.641023image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:26.318138image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.605546image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.188848image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.792405image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.378491image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.129312image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.726796image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:26.404936image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:22.692315image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.270372image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:23.883878image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:24.463270image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.218074image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:25.813666image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Correlations

2024-08-12T21:03:33.681920image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
AverageSpeedCircuitIDConstructorNameConstructorNationalityDifferenceGridPositionDriverNationalityGridNationalityPointsPositionRoundSeasonTop3Last5RacesTop5Last10RacesWonLast10RacesWonLast2RacesWonLast3RacesWonLast4RacesWonLast5RacesWonLast6RacesWonLast7RacesWonLastRaceconstructorIdcountry
AverageSpeed1.0000.5110.2180.1160.0750.101-0.1040.1010.214-0.2000.0650.4170.0930.0690.0000.0720.0540.0530.0510.0410.0290.0280.2180.474
CircuitID0.5111.0000.0580.0540.0400.0000.0000.0000.0310.0000.6560.2790.2300.2970.0910.1420.1220.1060.1070.1020.1110.0000.0581.000
ConstructorName0.2180.0581.0000.9990.1020.3760.3220.3760.2460.2280.0210.4350.4610.3600.1210.4360.3570.3230.2910.2610.2220.2921.0000.048
ConstructorNationality0.1160.0540.9991.0000.0680.3900.2510.3900.1960.1720.0000.2510.3420.2890.1220.3240.2690.2560.2300.2070.2050.2320.9990.038
DifferenceGridPosition0.0750.0400.1020.0681.0000.0600.4700.0600.235-0.398-0.001-0.0410.1820.1260.0440.1610.1430.1260.1120.0850.0730.1330.1020.035
DriverNationality0.1010.0000.3760.3900.0601.0000.1851.0000.1530.1320.0000.2770.2620.1960.1330.2610.2390.2250.2120.1970.1900.1920.3760.000
Grid-0.1040.0000.3220.2510.4700.1851.0000.185-0.5980.561-0.003-0.0590.4380.3190.1350.4660.4100.3580.2910.2490.2320.3550.3220.000
Nationality0.1010.0000.3760.3900.0601.0000.1851.0000.1530.1320.0000.2770.2620.1960.1330.2610.2390.2250.2120.1970.1900.1920.3760.000
Points0.2140.0310.2460.1960.2350.153-0.5980.1531.000-0.8770.0220.1840.4160.3430.2420.4300.3910.3700.3450.3380.3380.3240.2460.010
Position-0.2000.0000.2280.172-0.3980.1320.5610.132-0.8771.000-0.006-0.0380.4390.3180.1320.4410.3850.3360.2940.2630.2290.3200.2280.000
Round0.0650.6560.0210.000-0.0010.000-0.0030.0000.022-0.0061.0000.0930.2720.3410.1050.1670.1560.1450.1410.1360.1390.0000.0210.615
Season0.4170.2790.4350.251-0.0410.277-0.0590.2770.184-0.0380.0931.0000.0450.1020.0600.0500.0280.0300.0370.0370.0600.0000.4350.231
Top3Last5Races0.0930.2300.4610.3420.1820.2620.4380.2620.4160.4390.2720.0451.0000.6720.1900.6790.6380.5530.4780.4020.3280.3290.4610.214
Top5Last10Races0.0690.2970.3600.2890.1260.1960.3190.1960.3430.3180.3410.1020.6721.0000.2620.4890.4980.4880.4620.4590.4260.2370.3600.285
WonLast10Races0.0000.0910.1210.1220.0440.1330.1350.1330.2420.1320.1050.0600.1900.2621.0000.2110.2750.3340.3970.4710.5770.1950.1210.091
WonLast2Races0.0720.1420.4360.3240.1610.2610.4660.2610.4300.4410.1670.0500.6790.4890.2111.0000.7660.6300.5310.4470.3640.4760.4360.138
WonLast3Races0.0540.1220.3570.2690.1430.2390.4100.2390.3910.3850.1560.0280.6380.4980.2750.7661.0000.8210.6920.5830.4750.4640.3570.114
WonLast4Races0.0530.1060.3230.2560.1260.2250.3580.2250.3700.3360.1450.0300.5530.4880.3340.6300.8211.0000.8420.7090.5780.4320.3230.101
WonLast5Races0.0510.1070.2910.2300.1120.2120.2910.2120.3450.2940.1410.0370.4780.4620.3970.5310.6920.8421.0000.8400.6850.3940.2910.100
WonLast6Races0.0410.1020.2610.2070.0850.1970.2490.1970.3380.2630.1360.0370.4020.4590.4710.4470.5830.7090.8401.0000.8130.3550.2610.098
WonLast7Races0.0290.1110.2220.2050.0730.1900.2320.1900.3380.2290.1390.0600.3280.4260.5770.3640.4750.5780.6850.8131.0000.3360.2220.107
WonLastRace0.0280.0000.2920.2320.1330.1920.3550.1920.3240.3200.0000.0000.3290.2370.1950.4760.4640.4320.3940.3550.3361.0000.2920.000
constructorId0.2180.0581.0000.9990.1020.3760.3220.3760.2460.2280.0210.4350.4610.3600.1210.4360.3570.3230.2910.2610.2220.2921.0000.048
country0.4741.0000.0480.0380.0350.0000.0000.0000.0100.0000.6150.2310.2140.2850.0910.1380.1140.1010.1000.0980.1070.0000.0481.000

Missing values

2024-08-12T21:03:26.547525image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T21:03:26.898569image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SeasonRoundCircuitIDcountryConstructorNameconstructorIdConstructorNationalityDriverIDDriverNameDriverNationalityNationalityPositionPointsGridStatusAverageSpeedDifferenceGridPositionWonLastRaceWonLast2RacesWonLast3RacesWonLast4RacesWonLast5RacesWonLast6RacesWonLast7RacesWonLast10RacesTop3Last5RacesTop5Last10Races
020001albert_parkAustraliaArrowsarrowsBritishrosaPedroSpanishSpanish200.012Suspension0.0-80000000000
120001albert_parkAustraliaArrowsarrowsBritishverstappenJosDutchDutch180.013Suspension0.0-50000000000
220001albert_parkAustraliaBARbarBritishvilleneuveJacquesCanadianCanadian43.08Finished0.040000000000
320001albert_parkAustraliaBARbarBritishzontaRicardoBrazilianBrazilian61.016Finished0.0100000000000
420001albert_parkAustraliaBenettonbenettonItalianfisichellaGiancarloItalianItalian52.09Finished0.040000000000
520001albert_parkAustraliaBenettonbenettonItalianwurzAlexanderAustrianAustrian70.014Finished0.070000000000
620001albert_parkAustraliaFerrariferrariItalianbarrichelloRubensBrazilianBrazilian26.04Finished0.020000000000
720001albert_parkAustraliaFerrariferrariItalianmichael_schumacherMichaelGermanGerman110.03Finished0.020000000000
820001albert_parkAustraliaJaguarjaguarBritishherbertJohnnyBritishBritish220.020Clutch0.0-20000000000
920001albert_parkAustraliaJaguarjaguarBritishirvineEddieBritishBritish210.07Spun off0.0-140000000000
SeasonRoundCircuitIDcountryConstructorNameconstructorIdConstructorNationalityDriverIDDriverNameDriverNationalityNationalityPositionPointsGridStatusAverageSpeedDifferenceGridPositionWonLastRaceWonLast2RacesWonLast3RacesWonLast4RacesWonLast5RacesWonLast6RacesWonLast7RacesWonLast10RacesTop3Last5RacesTop5Last10Races
9869202414spaBelgiumMercedesmercedesGermanhamiltonLewisBritishBritish125.03Finished235.39920000000000
9870202414spaBelgiumMercedesmercedesGermanrussellGeorgeBritishBritish200.06Disqualified224.929-140000000000
9871202414spaBelgiumRB F1 TeamrbItalianricciardoDanielAustralianAustralian101.013Finished235.74330000000000
9872202414spaBelgiumRB F1 TeamrbItaliantsunodaYukiJapaneseJapanese160.020Finished235.60640000000000
9873202414spaBelgiumRed Bullred_bullAustrianmax_verstappenMaxDutchDutch412.011Finished235.61970111111011
9874202414spaBelgiumRed Bullred_bullAustrianperezSergioMexicanMexican77.02Finished237.057-50000000000
9875202414spaBelgiumSaubersauberSwissbottasValtteriFinnishFinnish150.014Finished233.795-10000000000
9876202414spaBelgiumSaubersauberSwisszhouGuanyuChineseChinese190.019Retired231.42200000000000
9877202414spaBelgiumWilliamswilliamsBritishalbonAlexanderThaiThai120.010Finished233.239-20000000000
9878202414spaBelgiumWilliamswilliamsBritishsargeantLoganAmericanAmerican170.018Finished233.53310000000000